ROC (Receiver operator curve) data from a classification random forest.

The sensitivity and specificity of a randomForest classification object.

Usage

# S3 method for class 'rfsrc'
gg_roc(object, which_outcome, oob, ...)

Arguments

object

an rfsrc classification object

which_outcome

select the classification outcome of interest.

oob

use oob estimates (default TRUE)

...

extra arguments (not used)

Value

gg_roc data.frame for plotting ROC curves.

See also

plot.gg_roc rfsrc randomForest

Examples

## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## -------- iris data
rfsrc_iris <- rfsrc(Species ~ ., data = iris)

# ROC for setosa
gg_dta <- gg_roc(rfsrc_iris, which_outcome = 1)
plot(gg_dta)


# ROC for versicolor
gg_dta <- gg_roc(rfsrc_iris, which_outcome = 2)
plot(gg_dta)


# ROC for virginica
gg_dta <- gg_roc(rfsrc_iris, which_outcome = 3)
plot(gg_dta)


## -------- iris data
rf_iris <- randomForest::randomForest(Species ~ ., data = iris)

# ROC for setosa
gg_dta <- gg_roc(rf_iris, which_outcome = 1)
plot(gg_dta)


# ROC for versicolor
gg_dta <- gg_roc(rf_iris, which_outcome = 2)
plot(gg_dta)


# ROC for virginica
gg_dta <- gg_roc(rf_iris, which_outcome = 3)
plot(gg_dta)